DIGITAL OFFICE TERMINAL
After finished onflight training on Entrepreneurialship and Digitalization, Ogulcan came to idea together with his friend to develop a mobile APP in support for all interational students traveling around the world...
Ogulcan Herel
INTERN @ HR & Training Department
Global approach style; It covers job satisfaction comprehensively. This way of examining job satisfaction simply asks employees if they are satisfied in general, using a single rating scale with a yes-no answer, using a small set of items that measure global job satisfaction. For example, satisfaction in general may be a component of several factors; wage satisfaction, type of job itself, working conditions, management type, business policies and procedures, relationships with colleagues, incentives and promotion opportunities.
Feature approach format; Feature approach is; takes into account that the job consists of feelings and attitudes about a number of different elements or characteristics. It addresses each of these aspects individually, assuming that certain employees may be more satisfied with some features, such as the amount of payment, but may not be satisfied with other features such as management quality and incentive opportunities. There has been controversy over which approach is better. Advocates of the global approach argue that an overall job satisfaction is important, and that overall satisfaction is more than the sum of satisfaction of individual job characteristics. They claim to provide a better and more detailed assessment of job satisfaction by allowing them to gain insight into certain individual feelings. Also, the more individual employees evaluate certain aspects of job satisfaction, big changes can occur. Satisfaction with wage may be an important element of job satisfaction for an employee, but not for another employee. Also, some features may not apply to all types of jobs. CEOs of businesses and self-employed professionals are not affected by opportunities to encourage divisions and managers in large enterprises — in particular, they can make a significant contribution to the low level of job satisfaction of lower-level managers in large enterprises. The trait approach advocates argue that this helps identify specific areas of dissatisfaction that are aimed for improvement. Others believe it is advantageous to use both types of measurement approaches, based on findings that show that each approach offers interesting and important information.
Ece Can - HR Deparment intern
Updated: May 16, 2021
On 30th January 2021 Saturday, I attended a presentation: “Digital Transformation of Factories / e-F@ctory / MAISART ”.
This event took place by Mitsubishi’s Product Management and Marketing unit manager Mr. Tolga Bizel. He is an Electrical and Electronics Engineer. I saw that event’s announcement on the Chamber of Electrical Engineers’ Linkedin page. These kinds of events are related with my close future profession that’s why I am always following and attending them.
First of all he explains what is MAISART, MAISART is “Mitsubishi Electric’s AI creates the State-of-the-ART in technology.” There are three Technologies that make up Maisart: Deep learning, Reinforcement learning, Big data analysis.
Deep learning: Compact algorithm. Implement high level AI for all equipment. There are machines which work like humans. Actually sometimes they work better than humans. The reason this machine Works better than humans is deep learning.
There is big data every single day about what we are doing with our phones, computers or some electronic devices which are connected to the internet. These movements are always recorded by the devices and collected somewhere.
They are always watching us and they record how we are doing what we are doing. After that this big data occurs. Everyone can process this data via algorithms and then that data becomes more sense for using. Every different field has its own algorithms. Because every factory or working place is doing different actions. So in short “deep learning” is an algorithm.
Reinforcement learning: Implementation of Mitsubishi’s AI in a short period of time by speedy learning. Reinforcement learning is a type of AI machine learning. Computers usually act following a human-created program. With reinforcement learning, however, a computer can understand the current situation by itself, set its own rules, and determine what action to take. Humans do not need to set the rules with a program. For a computer to determine what action to take next, it needs a lot of experiences, including experiences of failure, just as humans do.
These two ideas are really important: deep learning and reinforcement learning. We can separate them like this: in deep learning we giving to machines or computers a algorithm and they starting to interpret of big data and according algorithm that giving by factory’s manager and big data, they deciding on how they do of what they do. Deep learning is the first step of process.
For Reinforcement learning: After the starting progress, the machine encounters some problems like heating, lack of raw materials, lack of efficiency, machine part faults, in short anything that slows or stops the process. Machine analyzes those problems and it saves solutions and processes its own mind. Reinforcement learning is the part of AI that learns through the principle of “practice makes perfect.” It is the part of AI that finds success from failure.
Big Data Analysis: Make data analysis smarter in edge computing. Like I said before, Big data is broadly divided into data generated by humans (on social networks, for example) and data generated by things (such as sensors placed on equipment). The amount of data generated by things in particular is increasing rapidly as IoT spreads. Much attention is focusing on edge computing as a way to process this data quickly. This is because it would be very difficult to keep up with the explosive growth in data by existing means that depend on the cloud for all data processing, because that increases the data communications load and lowers responsiveness.
These three Technologies which Mitsubishi use are provides some features (that we will see in every factory soon):
-Examples of Alliance partners and E-Factory Collaboration-
This kind of factory uses digital twin simulation. When the actual factory while working also the digital factory working. Main goal of the digital twin is the early detection for anything that can make up for the lack of process. For example faults, or machine manager’s tiredness, condition of machine parts. Digital twin Works based on three technologies which we mentioned before.
-Time of Digital Factories-
-Smart Solution-
To sum it up, factories like Mitsubishi turned their gaze to the factories without human idea. That’s why everyday they are trying to be more humanless. Because it is more cheap, effective, fast and suited to human rights. On the other hand, I am wondering what are going to do as much as these people on earth without working, without Money… Because in today's condition you can not live without working. Maybe tomorrow we can find a more sensible and efficient way to solve problems like this…
Onur Camlica, volunteer@ICDET